AU_timeline %>%
filter(date <= ymd(20200515)) %>%
arrange(date) %>%
# filter(nchar(event) < 230) %>%
# filter(region != "VIC") %>%
mutate(rowid = as.numeric(rownames(.))) %>%
mutate(date_gap = as.numeric(date-lag(date))) %>%
mutate(nchar = nchar(event),
nchar_scaled = (nchar-min(nchar))/(max(nchar)-min(nchar)),
# point_position = 700 * nchar_scaled * (-1)^rowid,
point_position = c(100,100,280,320,550,20,100,550,720,150,
240,880,400,300)*(-1)^rowid
) %>%
mutate(facet_var = quarter(date, with_year = T)) %>%
ggplot(aes(x=date, y=0)) +
geom_line() +
geom_point(aes(col=region)) +
geom_segment(aes(x=date, xend=date, col=region,
y=0, yend=point_position)) +
geom_point(aes(y=point_position, col=region), shape=1) +
geom_text(aes(y=-10,
label=paste0(day(date), " ",
month(date, label=T))),
angle=30,
vjust=1, hjust=1) +
geom_label(aes(label=str_wrap(paste0(date, ": ", event), nchar/1.8), alpha=.7,
# directions="y", min.segment.length = Inf,
x=date, y=point_position, col=region, vjust="outward")) +
scale_x_date(date_breaks = "1 month",
date_labels = "%b %Y") +
# facet_wrap(~facet_var, strip.position="top",
# ncol = 1, scales = "free_x") +
# coord_flip() +
ylim(-1200,1200) +
coord_cartesian(clip = "off") +
labs(subtitle = "AU/NSW Covid-19 timeline, 1st half 2020") +
theme_void(base_size = 23) +
theme(legend.position = "none",
# rect = element_rect(fill = "transparent"),
plot.margin = unit(c(0, 2.3, 0, 2.3),"cm"),
# plot.background = element_rect(fill = NULL)
)AU_timeline %>%
filter(date > ymd(20200515)) %>%
arrange(date) %>%
# filter(nchar(event) < 230) %>%
# filter(region != "VIC") %>%
mutate(rowid = as.numeric(rownames(.))) %>%
mutate(date_gap = as.numeric(date-lag(date))) %>%
mutate(nchar = nchar(event),
nchar_scaled = (nchar-min(nchar))/(max(nchar)-min(nchar)),
# point_position = 700 * nchar_scaled * (-1)^rowid,
point_position = c(50,100,280,420,550,720,100,550,720,150,
240,780,830,300)*(-1)^rowid
) %>%
mutate(facet_var = quarter(date, with_year = T)) %>%
ggplot(aes(x=date, y=0)) +
geom_line() +
geom_point(aes(col=region)) +
geom_segment(aes(x=date, xend=date, col=region,
y=0, yend=point_position)) +
geom_point(aes(y=point_position, col=region), shape=1) +
geom_text(aes(y=-10,
label=paste0(day(date), " ",
month(date, label=T))),
angle=30,
vjust=1, hjust=1) +
geom_label(aes(label=str_wrap(paste0(date, ": ", event), nchar/3), alpha=.7,
# directions="y", min.segment.length = Inf,
x=date, y=point_position, col=region, vjust="outward")) +
scale_x_date(date_breaks = "1 month",
date_labels = "%b %Y") +
# facet_wrap(~facet_var, strip.position="top",
# ncol = 1, scales = "free_x") +
# coord_flip() +
ylim(-1200,1200) +
coord_cartesian(clip = "off") +
labs(subtitle = "AU/NSW Covid-19 timeline, 2nd half 2020") +
theme_void(base_size = 23) +
theme(legend.position = "none",
# rect = element_rect(fill = "transparent"),
plot.margin = unit(c(0, 2.3, 0, 2.3),"cm"),
# plot.background = element_rect(fill = NULL)
)AU_timeline %>%
# filter(date >= ymd(20200220)) %>%
arrange(date) %>%
add_row(date = ymd(20200120), event="") %>%
# add_row(date = ymd(20200220), event="") %>%
add_row(date = ymd(20200331), event="") %>%
add_row(date = ymd(20200401), event="") %>%
add_row(date = ymd(20200630), event="") %>%
add_row(date = ymd(20200701), event="") %>%
add_row(date = ymd(20200930), event="") %>%
add_row(date = ymd(20201001), event="") %>%
# filter(nchar(event) < 230) %>%
# filter(region != "VIC") %>%
mutate(rowid = as.numeric(rownames(.))) %>%
mutate(date_gap = as.numeric(date-lag(date))) %>%
mutate(nchar = nchar(event),
nchar_scaled = (nchar-min(nchar))/(max(nchar)-min(nchar)),
point_position = 800 * nchar_scaled * (-1)^rowid,
# point_position = c(100)*(-1)^rowid
) %>%
mutate(facet_var = quarter(date, with_year = T)) %>%
ggplot(aes(x=date, y=0)) +
geom_line() +
geom_point(aes(col=region)) +
geom_segment(aes(x=date, xend=date, col=region,
y=0, yend=point_position)) +
geom_point(aes(y=point_position, col=region), shape=1) +
geom_text(aes(y=-10,
label=paste0(day(date), " ",
month(date, label=T))),
angle=30,
vjust=1, hjust=1) +
geom_label(aes(label=str_wrap(paste0(date, ": ", event), nchar/2), alpha=.7,
# directions="y", min.segment.length = Inf,
x=date, y=point_position, col=region, vjust="outward")) +
scale_x_date(date_breaks = "1 month",
date_labels = "%b %Y") +
facet_wrap(~facet_var, strip.position="top",
ncol = 1, scales = "free_x") +
# coord_flip() +
ylim(-1200,1200) +
coord_cartesian(clip = "off") +
theme_void(base_size = 23) +
theme(legend.position = "none",
# rect = element_rect(fill = "transparent"),
plot.margin = unit(c(0, 2.3, 0, 2.3),"cm"),
# plot.background = element_rect(fill = NULL)
)confirmed_cases %>%
filter(as.character(postcode) %in% SYD_POA$POA_NAME16) %>%
count(notification_date) %>%
ggplot(aes(x=notification_date , y=n)) +
# geom_point() +
geom_line() +
ylab("Count of cases") +
labs(subtitle = "Daily new confirmed Covid-19 cases (SYD metro)") +
annotate("text", x=ymd(20200505), y=110, size=3.3, col="darkred",
label="1st outbreak \n (Bondi beach, \nSouth Eastern \nSydney)") +
annotate("text", x=ymd(20200815), y=33, size=3.3, col="darkred",
label="2nd outbreak \n (Westmead & Liverpool, \nWestern Sydney)") +
annotate("text", x=ymd(20210101), y=42, size=3.3, col="darkred",
label="3rd outbreak \n (Avalon beach, Northern Sydney)") +
ggl()confirmed_cases %>%
filter(as.character(postcode) %in% SYD_POA$POA_NAME16) %>%
count(lhd_2010_name, notification_date) %>%
ggplot(aes(x=notification_date , y=n, col=lhd_2010_name)) +
# geom_point() +
geom_line() +
# facet_wrap(~lhd_2010_name) +
labs(subtitle = "Daily new confirmed Covid-19 cases (SYD)") +
ggl(lp = "right")confirmed_cases %>%
filter(as.character(postcode) %in% SYD_POA$POA_NAME16) %>%
mutate(lhd_2010_name = ifelse(lhd_2010_name == "Sydney", "Metro Sydney",
lhd_2010_name)) %>%
# mutate(lhd_2010_name = ifelse(is.na(lhd_2010_name), "Unknown", lhd_2010_name)) %>%
count(lhd_2010_name, notification_date) %>%
group_by(lhd_2010_name) %>%
mutate(days_since_first_case = notification_date - min(notification_date)) %>%
mutate(accumulated_cases = cumsum(n)) %>%
mutate(lab = ifelse(notification_date == max(notification_date, na.rm=T),
lhd_2010_name, NA)) %>%
ggplot(aes(x=days_since_first_case , y=accumulated_cases, col=lhd_2010_name)) +
# geom_point() +
geom_line() +
geom_text_repel(aes(label=lab), size=4, hjust=-.1, min.segment.length = 10) +
# facet_wrap(~lhd_2010_name) +
labs(subtitle = "Accumulated confirmed Covid-19 cases (SYD), by region") +
xlim(c(0,550)) +
ggl(lp = "none") confirmed_cases %>%
mutate(lhd_2010_name = ifelse(lhd_2010_name == "Sydney", "Metro Sydney",
lhd_2010_name)) %>%
filter(as.character(postcode) %in% SYD_POA$POA_NAME16) %>%
# filter(lhd_2010_name %in% c("South Eastern Sydney",
# "Northern Sydney",
# "Western Sydney",
# "South Western Sydney",
# "Sydney") | is.na(lhd_2010_name)) %>%
count(lhd_2010_name, postcode, notification_date) %>%
group_by(lhd_2010_name, postcode) %>%
mutate(days_since_first_case = notification_date - min(notification_date)) %>%
mutate(accumulated_cases = cumsum(n)) %>%
mutate(lab=ifelse(notification_date == max(notification_date), postcode, NA)) %>%
ggplot(aes(x=days_since_first_case , y=accumulated_cases,
group=postcode,
col=lhd_2010_name)) +
# geom_point() +
geom_line() +
geom_text(aes(label=lab), size=3, hjust="outward") +
facet_wrap(~lhd_2010_name) +
labs(subtitle = "Accumulated confirmed Covid-19 cases (Sydney), by postcode") +
xlim(c(0,550)) +
ggl(lp = "none", ) +
theme(strip.text.x = element_text(size = 12))Looks like each postcode (POA) can only belong to one LGA, which is not the case for SA2
confirmed_cases %>%
distinct(postcode, lga_name19) %>%
count(postcode) %>%
summarise(max(n)) %>%
pull()[1] 1
confirmed_cases %>%
filter(lga_name19 %in% SYD_LGA$LGA_NAME19) %>%
count(lga_name19, name="Total_cases") %>%
rename(LGA_NAME19 = lga_name19) %>%
mutate(LGA_NAME19 = fct_reorder(LGA_NAME19, Total_cases)) %>%
plot_map_TL(SYD_LGA, "LGA_NAME19", "Total_cases",
"Total Covid-19 cases by LGA (SYD Metro)", show_count = T)confirmed_cases %>%
filter(as.character(postcode) %in% SYD_POA$POA_NAME16) %>%
count(postcode, name="Total_cases") %>%
rename(POA_NAME16 = postcode) %>%
mutate(POA_NAME16 = fct_reorder(as.factor(POA_NAME16), Total_cases)) %>%
plot_map_TL(SYD_POA, "POA_NAME16", "Total_cases",
"Total Covid-19 cases by POA (SYD Metro)",
show_count = T, label_size = 2)confirmed_cases %>%
filter(!(postcode %in% c(2026, 2145, 2170))) %>%
filter(as.character(postcode) %in% SYD_POA$POA_NAME16) %>%
count(postcode, name="Total_cases") %>%
rename(POA_NAME16 = postcode) %>%
mutate(POA_NAME16 = fct_reorder(as.factor(POA_NAME16), Total_cases)) %>%
plot_map_TL(SYD_POA, "POA_NAME16", "Total_cases",
"Total Covid-19 cases by POA (SYD Metro), excluding top 3 POA",
show_count = T, label_size = 2)Northern Beaches has many postcodes which result in more diluted choropleth by POA.
confirmed_cases %>%
distinct(lga_name19, postcode) %>%
count(lga_name19, name="Number_of_postcodes") %>%
left_join(confirmed_cases %>% count(lga_name19, name="Total_cases"),
by="lga_name19") %>%
slice_max(n=30, order_by = Total_cases) %>%
gather(key, value, -lga_name19) %>%
ggplot(aes(x=reorder(lga_name19, value), y=value)) +
geom_col() +
facet_wrap(~key, scales = "free_x") +
coord_flip() +
xlab("") + ylab("") +
ggl()confirmed_cases %>%
filter(as.character(postcode) %in% SYD_POA$POA_NAME16) %>%
mutate(year_quarter = paste(year(notification_date), "Q",
quarter(notification_date), sep="-")) %>%
count(year_quarter, postcode, name="Total_cases") %>%
complete(year_quarter, postcode, fill = list(Total_cases=0)) %>%
group_by(postcode) %>%
mutate(Accumulated_cases = cumsum(Total_cases)) %>%
group_by(year_quarter) %>%
mutate(Accumulated_share = (Accumulated_cases/sum(Accumulated_cases,
na.rm=T))) %>%
mutate(facet_var = paste0(year_quarter,
"\nAccumulated: ",
comma(sum(Accumulated_cases)))) %>%
rename(POA_NAME16 = postcode) %>%
plot_map_TL(rmapshaper::ms_simplify(
SYD_POA[SYD_POA$POA_NAME16 %in% as.character(
confirmed_cases$postcode) ,], .01),
"POA_NAME16", "Accumulated_cases",
"Total Covid-19 cases by POA (SYD Metro)",
return_obj="map") +
facet_wrap(~facet_var) +
theme(legend.position = "top")confirmed_cases %>%
filter(as.character(postcode) %in% SYD_POA$POA_NAME16) %>%
filter(year(notification_date) == 2020,
# month(notification_date) %in% 3:5,
isoweek(notification_date) %in% 10:23) %>%
filter(as.character(postcode) %in% SYD_POA$POA_NAME16) %>%
mutate(isoweek = isoweek(notification_date)) %>%
count(isoweek, postcode, name="Total_cases") %>%
complete(isoweek, postcode, fill = list(Total_cases=0)) %>%
group_by(postcode) %>%
mutate(Accumulated_cases = cumsum(Total_cases)) %>%
group_by(isoweek) %>%
mutate(Accumulated_share = (Accumulated_cases/sum(Accumulated_cases,
na.rm=T))) %>%
mutate(facet_var = paste0("week ", isoweek,
"\nAccumulated: ",
comma(sum(Accumulated_cases)))) %>%
rename(POA_NAME16 = postcode) %>%
mutate(POA_NAME16 = fct_reorder(as.factor(POA_NAME16), Total_cases)) %>%
ungroup() %>%
filter(!is.na(isoweek)) %>%
plot_map_TL(rmapshaper::ms_simplify(SYD_POA, .01),
"POA_NAME16", "Accumulated_cases",
"Total Covid-19 cases by POA (SYD Metro)",
return_obj="map") +
facet_wrap(~facet_var)confirmed_cases %>%
filter(as.character(postcode) %in% SYD_POA$POA_NAME16) %>%
count(postcode, name="Total_cases") %>%
rename(POA_NAME16 = postcode) %>%
mutate(POA_NAME16 = fct_reorder(as.factor(POA_NAME16), Total_cases)) %>%
plot_map_TL(SYD_POA, "POA_NAME16", "Total_cases", fill_col = "grey33",
"Total Covid-19 cases by POA (SYD Metro), with public transport",
show_count = T, label_size = 2, return_obj = "map") +
# geom_map(inherit.aes = FALSE, alpha=.85,
# aes(map_id = id), map = tidy(SYD_POA),
# col="grey50", size=.3, fill="white") +
geom_line(data = SYD_trains,
aes(x=lon, y=lat, col="Train",
group = `Railway line(s)`),
size=.8) +
geom_line(data = SYD_ferries,
aes(x=lon, y=lat, col="Ferry"),
size=.8) +
geom_line(data = SYD_lightrails,
aes(x=lon, y=lat, col="Lightrail"),
size=.8) +
geom_line(data = SYD_metro,
aes(x=lon, y=lat, col="Metro"),
size=.8) +
ggc(c("deepskyblue", "red", "darkgreen", "orange")) +
guides(col=guide_legend(title="Transport_mode")) +
expand_limits(x = tidy(SYD_POA)$long, y = tidy(SYD_POA)$lat) +
coord_cartesian(xlim = c(150.7, 151.48), ylim = c(-34.1, -33.5)) +
theme_void() +
theme(legend.position = c(.9,.35))bind_rows(
SYD_shops %>% select(lon, lat) %>%
rename(Long = lon, Lat = lat) %>% mutate(obj = "Shopping centres"),
SYD_supermarkets %>% select(lon, lat) %>%
rename(Long = lon, Lat = lat) %>% mutate(obj = "Supermarkets"),
SYD_hospitals %>% select(Longitude, Latitude) %>%
rename(Long = Longitude, Lat = Latitude) %>% mutate(obj = "Hospitals"),
rbind(SYD_sschools %>% select(Long, Lat),
SYD_pschools %>% select(Long, Lat)) %>% mutate(obj = "Schools")
) %>%
ggplot() +
geom_map(data = SYD_POA, inherit.aes = FALSE, alpha=.85,
aes(map_id = id), map = tidy(SYD_POA),
col="grey50", size=.2, fill="white") +
geom_jitter(aes(x=Long, y=Lat, col=obj), size=.8, alpha=.5) +
stat_density2d(aes(x=Long, y=Lat,
fill=..level.., alpha=..level.., col=obj),
binwidth = 1.2, geom="polygon", size=.23) +
facet_wrap(~obj) +
expand_limits(x = tidy(SYD_POA)$long, y = tidy(SYD_POA)$lat) +
xlim(150.7,151.48) + ylim(-34.1,-33.5) +
scale_fill_gradient(low="white", high=DC[7]) +
scale_alpha_continuous(range = c(0,.3)) +
ggc(fc = c("darkred", "deepskyblue", "darkblue", "darkgreen")) +
theme_void() +
theme(legend.position = "none",
legend.title = element_blank(),
strip.text = element_text(size=15))confirmed_cases %>%
filter(as.character(postcode) %in% SYD_POA$POA_NAME16) %>%
count(postcode, name="Total_cases") %>%
rename(POA_NAME16 = postcode) %>%
mutate(POA_NAME16 = fct_reorder(as.factor(POA_NAME16), Total_cases)) %>%
plot_map_TL(SYD_POA, "POA_NAME16", "Total_cases", fill_col = "grey33",
"Total Covid-19 cases by POA (SYD Metro), with hospitals, schools & supermarkets",
show_count = T, label_size = 2, return_obj = "map") +
# geom_map(data = SYD_POA, inherit.aes = FALSE, alpha=.85,
# aes(map_id = id), map = tidy(SYD_POA),
# col="grey50", size=.3, fill="white") +
geom_jitter(data = bind_rows(
SYD_shops %>% select(lon, lat) %>%
rename(Long = lon, Lat = lat) %>% mutate(obj = "Shopping centres"),
SYD_supermarkets %>% select(lon, lat) %>%
rename(Long = lon, Lat = lat) %>% mutate(obj = "Supermarkets"),
SYD_hospitals %>% select(Longitude, Latitude) %>%
rename(Long = Longitude, Lat = Latitude) %>% mutate(obj = "Hospitals"),
rbind(SYD_sschools %>% select(Long, Lat),
SYD_pschools %>% select(Long, Lat)) %>% mutate(obj = "Schools")
),
size = 1, alpha = .3, aes(x=Long, y=Lat,col=obj)) +
expand_limits(x = tidy(SYD_POA)$long, y = tidy(SYD_POA)$lat) +
xlim(150.7,151.48) + ylim(-34.1,-33.5) +
# scale_fill_gradient(low="white", high=DC[7]) +
scale_alpha_continuous(range = c(0,.3)) +
ggc(fc = c("darkred", "darkblue", "darkorange", "darkgreen")) +
guides(color = guide_legend(override.aes = list(size=3),
title="POI")) +
theme_void() +
theme(legend.position = c(.9,.25),
# legend.title = element_blank(),
strip.text = element_text(size=15))confirmed_cases %>%
filter(as.character(postcode) %in% SYD_POA$POA_NAME16) %>%
count(postcode, name="Total_cases") %>%
rename(POA_NAME16 = postcode) %>%
mutate(POA_NAME16 = fct_reorder(as.factor(POA_NAME16), Total_cases)) %>%
plot_map_TL(SYD_POA, "POA_NAME16", "Total_cases", fill_col = "grey33",
"Total Covid-19 cases by POA (SYD Metro), with hospitals, schools & supermarkets",
show_count = T, label_size = 2, return_obj = "map") +
geom_line(data = SYD_trains,
aes(x=lon, y=lat, col="Train",
group = `Railway line(s)`),
size=.8) +
geom_line(data = SYD_ferries,
aes(x=lon, y=lat, col="Ferry"),
size=.8) +
geom_line(data = SYD_lightrails,
aes(x=lon, y=lat, col="Lightrail"),
size=.8) +
geom_line(data = SYD_metro,
aes(x=lon, y=lat, col="Metro"),
size=.8) +
# geom_map(data = SYD_POA, inherit.aes = FALSE, alpha=.85,
# aes(map_id = id), map = tidy(SYD_POA),
# col="grey50", size=.3, fill="white") +
geom_jitter(data = bind_rows(
SYD_shops %>% select(lon, lat) %>%
rename(Long = lon, Lat = lat) %>% mutate(obj = "Shopping centres"),
SYD_supermarkets %>% select(lon, lat) %>%
rename(Long = lon, Lat = lat) %>% mutate(obj = "Supermarkets"),
SYD_hospitals %>% select(Longitude, Latitude) %>%
rename(Long = Longitude, Lat = Latitude) %>% mutate(obj = "Hospitals"),
rbind(SYD_sschools %>% select(Long, Lat),
SYD_pschools %>% select(Long, Lat)) %>% mutate(obj = "Schools")
),
size = 1, alpha = .3, aes(x=Long, y=Lat,col=obj)) +
expand_limits(x = tidy(SYD_POA)$long, y = tidy(SYD_POA)$lat) +
xlim(150.7,151.48) + ylim(-34.1,-33.5) +
# scale_fill_gradient(low="white", high=DC[7]) +
scale_alpha_continuous(range = c(0,.3)) +
ggc(fc = c("deepskyblue", "darkblue", "red", "darkgreen",
"darkblue", "red", "darkgreen", "orange")) +
guides(color = guide_legend(override.aes = list(size=3),
title="POI")) +
theme_void() +
theme(legend.position = c(.9,.4),
# legend.title = element_blank(),
strip.text = element_text(size=15))n_poi_by_POA %>%
filter(POA_NAME16 != "Unknown") %>%
# gather(key, value, -POA_NAME16) %>%
select(POA_NAME16, n_hospitals) %>%
mutate(POA_NAME16 = fct_reorder(POA_NAME16, n_hospitals)) %>%
plot_map_TL(SYD_POA, "POA_NAME16", "n_hospitals",
"Number of hospitals by POA",
show_count = T, fill_col = "darkred")n_poi_by_POA %>%
filter(POA_NAME16 != "Unknown") %>%
# gather(key, value, -POA_NAME16) %>%
select(POA_NAME16, n_schools) %>%
mutate(POA_NAME16 = fct_reorder(POA_NAME16, n_schools)) %>%
plot_map_TL(SYD_POA, "POA_NAME16", "n_schools",
"Number of schools by POA",
show_count = T, fill_col = "deepskyblue")n_poi_by_POA %>%
filter(POA_NAME16 != "Unknown") %>%
# gather(key, value, -POA_NAME16) %>%
select(POA_NAME16, n_supermarkets) %>%
mutate(POA_NAME16 = fct_reorder(POA_NAME16, n_supermarkets)) %>%
plot_map_TL(SYD_POA, "POA_NAME16", "n_supermarkets",
"Number of supermarkets by POA",
show_count = T, fill_col = "darkgreen")n_poi_by_POA %>%
filter(POA_NAME16 != "Unknown") %>%
# gather(key, value, -POA_NAME16) %>%
select(POA_NAME16, n_shoppingCentres) %>%
mutate(POA_NAME16 = fct_reorder(POA_NAME16, n_shoppingCentres)) %>%
plot_map_TL(SYD_POA, "POA_NAME16", "n_shoppingCentres",
"Number of shopping centres by POA",
show_count = T, fill_col = "black")n_poi_by_POA %>%
filter(POA_NAME16 != "Unknown") %>%
# gather(key, value, -POA_NAME16) %>%
select(POA_NAME16, n_publicTransports ) %>%
mutate(POA_NAME16 = fct_reorder(POA_NAME16, n_publicTransports )) %>%
plot_map_TL(SYD_POA, "POA_NAME16", "n_publicTransports",
"Number of public transports by POA",
show_count = T, fill_col = "darkorange")(
plot_gravity_map_TL(n_poi_by_POA, "2026", "n_hospitals") +
xlim(151.1,151.33) + ylim(-34,-33.7) |
plot_gravity_map_TL(n_poi_by_POA, "2026", "n_schools") +
xlim(151.1,151.33) + ylim(-34,-33.7) |
plot_gravity_map_TL(n_poi_by_POA, "2026", "n_supermarkets") +
xlim(151.1,151.33) + ylim(-34,-33.7)
) /
(
plot_gravity_map_TL(n_poi_by_POA, "2026", "n_shoppingCentres") +
xlim(151.1,151.33) + ylim(-34,-33.7) |
plot_gravity_map_TL(n_poi_by_POA, "2026", "n_publicTransports") +
xlim(151.1,151.33) + ylim(-34,-33.7) |
n_poi_by_POA %>%
mutate(combined_index =
normalised_n_hospitals +
normalised_n_schools +
normalised_n_supermarkets +
normalised_n_shoppingCentres +
normalised_n_publicTransports
) %>% plot_gravity_map_TL("2026", "combined_index") +
xlim(151.1,151.33) + ylim(-34,-33.7)
)(
plot_gravity_map_TL(n_poi_by_POA, "2026", "n_hospitals",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) +
xlim(151.1,151.33) + ylim(-34,-33.7) |
plot_gravity_map_TL(n_poi_by_POA, "2026", "n_schools",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) +
xlim(151.1,151.33) + ylim(-34,-33.7) |
plot_gravity_map_TL(n_poi_by_POA, "2026", "n_supermarkets",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) +
xlim(151.1,151.33) + ylim(-34,-33.7)
) /
(
plot_gravity_map_TL(n_poi_by_POA, "2026", "n_shoppingCentres",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) +
xlim(151.1,151.33) + ylim(-34,-33.7) |
plot_gravity_map_TL(n_poi_by_POA, "2026", "n_publicTransports",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) +
xlim(151.1,151.33) + ylim(-34,-33.7) |
n_poi_by_POA %>%
mutate(combined_index =
normalised_n_hospitals +
normalised_n_schools +
normalised_n_supermarkets +
normalised_n_shoppingCentres +
normalised_n_publicTransports
) %>% plot_gravity_map_TL("2026", "combined_index",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) +
xlim(151.1,151.33) + ylim(-34,-33.7)
)(
plot_gravity_map_TL(n_poi_by_POA, "2145", "n_hospitals") |
plot_gravity_map_TL(n_poi_by_POA, "2145", "n_schools") |
plot_gravity_map_TL(n_poi_by_POA, "2145", "n_supermarkets")
) /
(
plot_gravity_map_TL(n_poi_by_POA, "2145", "n_shoppingCentres") |
plot_gravity_map_TL(n_poi_by_POA, "2145", "n_publicTransports") |
n_poi_by_POA %>%
mutate(combined_index =
normalised_n_hospitals +
normalised_n_schools +
normalised_n_supermarkets +
normalised_n_shoppingCentres +
normalised_n_publicTransports
) %>% plot_gravity_map_TL("2145", "combined_index")
)(
plot_gravity_map_TL(n_poi_by_POA, "2145", "n_hospitals",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) |
plot_gravity_map_TL(n_poi_by_POA, "2145", "n_schools",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) |
plot_gravity_map_TL(n_poi_by_POA, "2145", "n_supermarkets",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
) /
(
plot_gravity_map_TL(n_poi_by_POA, "2145", "n_shoppingCentres",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) |
plot_gravity_map_TL(n_poi_by_POA, "2145", "n_publicTransports",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) |
n_poi_by_POA %>%
mutate(combined_index =
normalised_n_hospitals +
normalised_n_schools +
normalised_n_supermarkets +
normalised_n_shoppingCentres +
normalised_n_publicTransports
) %>% plot_gravity_map_TL("2145", "combined_index",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
)(
plot_gravity_map_TL(n_poi_by_POA, "2107", "n_hospitals") +
xlim(151,151.4) + ylim(-33.9,-33.5) |
plot_gravity_map_TL(n_poi_by_POA, "2107", "n_schools") +
xlim(151,151.4) + ylim(-33.9,-33.5) |
plot_gravity_map_TL(n_poi_by_POA, "2107", "n_supermarkets") +
xlim(151,151.4) + ylim(-33.9,-33.5)
) /
(
plot_gravity_map_TL(n_poi_by_POA, "2107", "n_shoppingCentres") +
xlim(151,151.4) + ylim(-33.9,-33.5) |
plot_gravity_map_TL(n_poi_by_POA, "2107", "n_publicTransports") +
xlim(151,151.4) + ylim(-33.9,-33.5) |
n_poi_by_POA %>%
mutate(combined_index =
normalised_n_hospitals +
normalised_n_schools +
normalised_n_supermarkets +
normalised_n_shoppingCentres +
normalised_n_publicTransports
) %>% plot_gravity_map_TL("2107", "combined_index") +
xlim(151,151.4) + ylim(-33.9,-33.5)
)(
plot_gravity_map_TL(n_poi_by_POA, "2107", "n_hospitals",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) +
xlim(151,151.4) + ylim(-33.9,-33.5) |
plot_gravity_map_TL(n_poi_by_POA, "2107", "n_schools",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) +
xlim(151,151.4) + ylim(-33.9,-33.5) |
plot_gravity_map_TL(n_poi_by_POA, "2107", "n_supermarkets",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) +
xlim(151,151.4) + ylim(-33.9,-33.5)
) /
(
plot_gravity_map_TL(n_poi_by_POA, "2107", "n_shoppingCentres",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) +
xlim(151,151.4) + ylim(-33.9,-33.5) |
plot_gravity_map_TL(n_poi_by_POA, "2107", "n_publicTransports",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) +
xlim(151,151.4) + ylim(-33.9,-33.5) |
n_poi_by_POA %>%
mutate(combined_index =
normalised_n_hospitals +
normalised_n_schools +
normalised_n_supermarkets +
normalised_n_shoppingCentres +
normalised_n_publicTransports
) %>% plot_gravity_map_TL("2107", "combined_index",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000)) +
xlim(151,151.4) + ylim(-33.9,-33.5)
)bind_rows(
calc_gravity_TL(n_poi_by_POA, "2026", "n_hospitals",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
,calc_gravity_TL(n_poi_by_POA, "2026", "n_schools",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
,calc_gravity_TL(n_poi_by_POA, "2026", "n_supermarkets",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
,calc_gravity_TL(n_poi_by_POA, "2026", "n_shoppingCentres",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
,calc_gravity_TL(n_poi_by_POA, "2026", "n_publicTransports",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
) %>%
group_by(target, source) %>%
summarise(risk_factor = sum(gravity_rank)) %>%
# slice_max(risk_factor, n=11) %>%
# pdf()
left_join(
confirmed_cases %>%
filter(notification_date < ymd(20200701)) %>%
count(postcode, name = "total_cases") %>%
mutate(source = as.character(postcode)),
by="source"
) %>%
mutate(text_lab = ifelse(total_cases >= 30, source, NA)) %>%
ggplot(aes(x=risk_factor, y=total_cases)) +
geom_point() +
stat_smooth() +
geom_text_repel(aes(label=text_lab)) +
labs(subtitle = "Based on total cases before date 2020-07-01") +
ggl(base_size=12)bind_rows(
calc_gravity_TL(n_poi_by_POA, "2145", "n_hospitals",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
,calc_gravity_TL(n_poi_by_POA, "2145", "n_schools",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
,calc_gravity_TL(n_poi_by_POA, "2145", "n_supermarkets",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
,calc_gravity_TL(n_poi_by_POA, "2145", "n_shoppingCentres",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
,calc_gravity_TL(n_poi_by_POA, "2145", "n_publicTransports",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
) %>%
group_by(target, source) %>%
summarise(risk_factor = sum(gravity_rank)) %>%
# slice_max(risk_factor, n=11) %>%
# pdf()
left_join(
confirmed_cases %>%
filter(notification_date >= ymd(20200701),
notification_date < ymd(20201101)) %>%
count(postcode, name = "total_cases") %>%
mutate(source = as.character(postcode)),
by="source"
) %>%
mutate(text_lab = ifelse(total_cases >= 15, source, NA)) %>%
ggplot(aes(x=risk_factor, y=total_cases)) +
geom_point() +
stat_smooth() +
geom_text_repel(aes(label=text_lab)) +
labs(subtitle = "Based on total cases between date 2020-07-01 and 2020-12-01") +
ggl(base_size=12)bind_rows(
calc_gravity_TL(n_poi_by_POA, "2107", "n_hospitals",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
,calc_gravity_TL(n_poi_by_POA, "2107", "n_schools",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
,calc_gravity_TL(n_poi_by_POA, "2107", "n_supermarkets",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
,calc_gravity_TL(n_poi_by_POA, "2107", "n_shoppingCentres",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
,calc_gravity_TL(n_poi_by_POA, "2107", "n_publicTransports",
dist_matrix = SYD_POA_mapdist %>% mutate(dist = dist/1000))
) %>%
group_by(target, source) %>%
summarise(risk_factor = sum(gravity_rank)) %>%
# slice_max(risk_factor, n=11) %>%
# pdf()
left_join(
confirmed_cases %>%
filter(notification_date >= ymd(20201101)) %>%
count(postcode, name = "total_cases") %>%
mutate(source = as.character(postcode)),
by="source"
) %>%
mutate(text_lab = ifelse(total_cases >= 15, source, NA)) %>%
ggplot(aes(x=risk_factor, y=total_cases)) +
geom_point() +
stat_smooth() +
geom_text_repel(aes(label=text_lab)) +
labs(subtitle = "Based on total cases after date 2020-12-01") +
ggl(base_size=12)SYD_POA_mapdist %>%
filter(target == "2145") %>%
left_join(n_poi_by_POA, by=c("source"="POA_NAME16")) %>%
left_join(n_poi_by_POA %>% filter(POA_NAME16 == "2145"),
suffix = c("","_target"),
by=c("target"="POA_NAME16")) %>%
left_join(confirmed_cases %>%
filter(notification_date >= ymd(20200701),
notification_date < ymd(20201101)) %>%
count(postcode, name="Total_cases") %>%
mutate(source = as.character(postcode)), by="source") %>%
mutate(total_normalised_mass_source =
normalised_n_hospitals +
normalised_n_schools +
normalised_n_supermarkets +
normalised_n_shoppingCentres +
normalised_n_publicTransports) %>%
lm(log(Total_cases + 0.01) ~
# log(total_normalised_mass_source + 0.01) +
log(n_hospitals + 0.01) +
log(n_schools + 0.01) +
log(n_supermarkets + 0.01) +
log(n_shoppingCentres + 0.01) +
log(n_publicTransports + 0.01) +
log(n_hospitals + 0.01) +
log(dist + 0.01), data=.) %>%
summary()
Call:
lm(formula = log(Total_cases + 0.01) ~ log(n_hospitals + 0.01) +
log(n_schools + 0.01) + log(n_supermarkets + 0.01) + log(n_shoppingCentres +
0.01) + log(n_publicTransports + 0.01) + log(n_hospitals +
0.01) + log(dist + 0.01), data = .)
Residuals:
Min 1Q Median 3Q Max
-1.42928 -0.69210 -0.04544 0.51836 1.96975
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.164653 0.382780 5.655 0.0000000872 ***
log(n_hospitals + 0.01) 0.003754 0.032853 0.114 0.9092
log(n_schools + 0.01) 0.192696 0.075626 2.548 0.0119 *
log(n_supermarkets + 0.01) 0.042677 0.032916 1.297 0.1970
log(n_shoppingCentres + 0.01) 0.014014 0.036279 0.386 0.6999
log(n_publicTransports + 0.01) 0.027427 0.029995 0.914 0.3621
log(dist + 0.01) -0.385944 0.092691 -4.164 0.0000550474 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8256 on 137 degrees of freedom
(70 observations deleted due to missingness)
Multiple R-squared: 0.2365, Adjusted R-squared: 0.2031
F-statistic: 7.074 on 6 and 137 DF, p-value: 0.000001406